Package 'climaemet' reference manual

Title:	Climate AEMET Tools
Description:	Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).
Authors:	Manuel Pizarro [aut, cph] , Diego Hernangómez [aut, cre] (<https://orcid.org/0000-0001-8457-4658>, rOpenSpain), Gema Fernández-Avilés [aut]
Maintainer:	Diego Hernangómez <[email protected]>
License:	GPL-3
Version:	1.3.0
Built:	2024-07-26 15:15:47 UTC
Source:	https://github.com/rOpenSpain/climaemet

Install an AEMET API Key

Description

This function will store your AEMET API key on your local machine so it can be called securely without being stored in your code.

Alternatively, you can install the API Key manually:

Run Sys.setenv(AEMET_API_KEY = "Your_Key"). You would need to run this command on each session (Similar to install = FALSE).
Write this line on your .Renviron file: AEMET_API_KEY = "Your_Key" (same behavior than install = TRUE). This would store your API key permanently.

Usage

aemet_api_key(apikey, overwrite = FALSE, install = FALSE)
aemet_api_key(apikey, overwrite = FALSE, install = FALSE)

Arguments

`apikey`	The API key provided to you from the AEMET formatted in quotes. A key can be acquired at https://opendata.aemet.es/centrodedescargas/inicio. You can install several API Keys as a vector of characters, see Details.
`overwrite`	If this is set to `TRUE`, it will overwrite an existing `AEMET_API_KEY` that you already have in local machine.
`install`	if `TRUE`, will install the key in your local machine for use in future sessions. Defaults to `FALSE.`

Details

You can pass several apikey values as a vector c(api1, api2), in this case several AEMET_API_KEY values would be generated. In each subsequent api call climaemet would randomly choose one of the provided API keys.

This is useful when performing batch queries to avoid API throttling.

Value

None

Note

To locate your API Key on your local machine, run rappdirs::user_cache_dir("climaemet", "R").

Examples

# Don't run these examples!

if (FALSE) {
  aemet_api_key("111111abc", install = TRUE)

  # You can check it with:
  Sys.getenv("AEMET_API_KEY")
}

if (FALSE) {
  # If you need to overwrite an existing key:
  aemet_api_key("222222abc", overwrite = TRUE, install = TRUE)

  # You can check it with:
  Sys.getenv("AEMET_API_KEY")
}
# Don't run these examples!

if (FALSE) {
  aemet_api_key("111111abc", install = TRUE)

  # You can check it with:
  Sys.getenv("AEMET_API_KEY")
}

if (FALSE) {
  # If you need to overwrite an existing key:
  aemet_api_key("222222abc", overwrite = TRUE, install = TRUE)

  # You can check it with:
  Sys.getenv("AEMET_API_KEY")
}

AEMET beaches

Description

Get AEMET beaches.

Usage

aemet_beaches(verbose = FALSE, return_sf = FALSE)
aemet_beaches(verbose = FALSE, return_sf = FALSE)

Arguments

`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.

Details

The first result of the API call on each session is (temporarily) cached in the assigned tempdir() for avoiding unneeded API calls.

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples


library(tibble)
beaches <- aemet_beaches()
beaches

# Cached during this R session
beaches2 <- aemet_beaches(verbose = TRUE)

identical(beaches, beaches2)

# Select an map beaches
library(dplyr)
library(ggplot2)
library(mapSpain)

# Alicante / Alacant
beaches_sf <- aemet_beaches(return_sf = TRUE) %>%
  filter(ID_PROVINCIA == "03")

prov <- mapSpain::esp_get_prov("Alicante")

ggplot(prov) +
  geom_sf() +
  geom_sf(
    data = beaches_sf, shape = 4, size = 2.5,
    color = "blue"
  )

library(tibble)
beaches <- aemet_beaches()
beaches

# Cached during this R session
beaches2 <- aemet_beaches(verbose = TRUE)

identical(beaches, beaches2)

# Select an map beaches
library(dplyr)
library(ggplot2)
library(mapSpain)

# Alicante / Alacant
beaches_sf <- aemet_beaches(return_sf = TRUE) %>%
  filter(ID_PROVINCIA == "03")

prov <- mapSpain::esp_get_prov("Alicante")

ggplot(prov) +
  geom_sf() +
  geom_sf(
    data = beaches_sf, shape = 4, size = 2.5,
    color = "blue"
  )

Daily/annual climatology values

Description

Get climatology values for a station or for all the available stations. Note that aemet_daily_period() and aemet_daily_period_all() are shortcuts of aemet_daily_clim().

Usage

aemet_daily_clim(
  station = "all",
  start = Sys.Date() - 7,
  end = Sys.Date(),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period(
  station,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_daily_clim(
  station = "all",
  start = Sys.Date() - 7,
  end = Sys.Date(),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period(
  station,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`) or "all" for all the stations.
`start`, `end`	Character string with start and end date. See Details.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.

Details

start and end parameters should be:

For aemet_daily_clim(): A Date object or a string with format: YYYY-MM-DD ("2020-12-31") coercible with as.Date().
For aemet_daily_period() and aemet_daily_period_all(): A string representing the year(s) to be extracted: "2020", "2018".

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples



library(tibble)
obs <- aemet_daily_clim(c("9434", "3195"))
glimpse(obs)

# Metadata
meta <- aemet_daily_clim(c("9434", "3195"), extract_metadata = TRUE)

glimpse(meta$campos)

library(tibble)
obs <- aemet_daily_clim(c("9434", "3195"))
glimpse(obs)

# Metadata
meta <- aemet_daily_clim(c("9434", "3195"), extract_metadata = TRUE)

glimpse(meta$campos)

Check if an AEMET API Key is present for the current session

Description

The function would detect if an API Key is available on this session:

If an API Key is already set as an environment variable it would be preserved
If no environment variable has been set and you have stored permanently an API Key using aemet_api_key(), the latter would be loaded.

Usage

aemet_detect_api_key(...)

aemet_show_api_key(...)
aemet_detect_api_key(...)

aemet_show_api_key(...)

Arguments

...

Ignored

Value

TRUE or FALSE. aemet_show_api_key() would display your stored API keys.

Examples


aemet_detect_api_key()

# CAUTION: This may reveal API Keys
if (FALSE) {
  aemet_show_api_key()
}
aemet_detect_api_key()

# CAUTION: This may reveal API Keys
if (FALSE) {
  aemet_show_api_key()
}

Extreme values for a station

Description

Get recorded extreme values for a station.

Usage

aemet_extremes_clim(
  station = NULL,
  parameter = "T",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_extremes_clim(
  station = NULL,
  parameter = "T",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`).
`parameter`	Character string as temperature (`"T"`), precipitation (`"P"`) or wind (`"V"`) parameter.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.

Value

A tibble or a sf object. If the function finds an error when parsing it would return the result as a list() object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples


library(tibble)
obs <- aemet_extremes_clim(c("9434", "3195"))
glimpse(obs)

library(tibble)
obs <- aemet_extremes_clim(c("9434", "3195"))
glimpse(obs)

Forecast database for beaches

Description

Get a database of daily weather forecasts for a beach. Beach database can be accessed with aemet_beaches().

Usage

aemet_forecast_beaches(
  x,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_forecast_beaches(
  x,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`x`	A vector of beaches codes to extract. See `aemet_beaches()`.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples


# Forecast for beaches in Palma, Mallorca
library(dplyr)
library(ggplot2)

palma_b <- aemet_beaches() %>%
  filter(ID_MUNICIPIO == "07040")

forecast_b <- aemet_forecast_beaches(palma_b$ID_PLAYA)
glimpse(forecast_b)

ggplot(forecast_b) +
  geom_line(aes(fecha, tagua_valor1, color = nombre)) +
  facet_wrap(~nombre, ncol = 1) +
  labs(
    title = "Water temperature in beaches of Palma (ES)",
    subtitle = "Forecast 3-days",
    x = "Date",
    y = "Temperature (Celsius)",
    color = "Beach"
  )

# Forecast for beaches in Palma, Mallorca
library(dplyr)
library(ggplot2)

palma_b <- aemet_beaches() %>%
  filter(ID_MUNICIPIO == "07040")

forecast_b <- aemet_forecast_beaches(palma_b$ID_PLAYA)
glimpse(forecast_b)

ggplot(forecast_b) +
  geom_line(aes(fecha, tagua_valor1, color = nombre)) +
  facet_wrap(~nombre, ncol = 1) +
  labs(
    title = "Water temperature in beaches of Palma (ES)",
    subtitle = "Forecast 3-days",
    x = "Date",
    y = "Temperature (Celsius)",
    color = "Beach"
  )

Forecast database by municipality

Description

Get a database of daily or hourly weather forecasts for a given municipality.

Usage

aemet_forecast_daily(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_forecast_hourly(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_forecast_daily(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_forecast_hourly(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`x`	A vector of municipality codes to extract. For convenience, climaemet provides this data on the dataset aemet_munic (see `municipio` field) as of January 2024.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.

Details

Forecasts format provided by the AEMET API have a complex structure. Although climaemet returns a tibble, each forecasted value is provided as a nested tibble. aemet_forecast_tidy() helper function can unnest these values an provide a single unnested tibble for the requested variable.

If extract_metadata = TRUE a simple tibble describing the value of each field of the forecast is returned.

Value

A nested tibble. Forecasted values can be extracted with aemet_forecast_tidy(). See also Details.

API Key

You need to set your API Key globally using aemet_api_key().

Examples



# Select a city
data("aemet_munic")
library(dplyr)
munis <- aemet_munic %>%
  filter(municipio_nombre %in% c("Santiago de Compostela", "Lugo")) %>%
  pull(municipio)

daily <- aemet_forecast_daily(munis)

# Metadata
meta <- aemet_forecast_daily(munis, extract_metadata = TRUE)
glimpse(meta$campos)

# Vars available
aemet_forecast_vars_available(daily)


# This is nested
daily %>%
  select(municipio, fecha, nombre, temperatura)

# Select and unnest
daily_temp <- aemet_forecast_tidy(daily, "temperatura")

# This is not
daily_temp

# Wrangle and plot
daily_temp_end <- daily_temp %>%
  select(
    elaborado, fecha, municipio, nombre, temperatura_minima,
    temperatura_maxima
  ) %>%
  tidyr::pivot_longer(cols = contains("temperatura"))

# Plot
library(ggplot2)
ggplot(daily_temp_end) +
  geom_line(aes(fecha, value, color = name)) +
  facet_wrap(~nombre, ncol = 1) +
  scale_color_manual(
    values = c("red", "blue"),
    labels = c("max", "min")
  ) +
  scale_x_date(
    labels = scales::label_date_short(),
    breaks = "day"
  ) +
  scale_y_continuous(
    labels = scales::label_comma(suffix = "º")
  ) +
  theme_minimal() +
  labs(
    x = "", y = "",
    color = "",
    title = "Forecast: 7-day temperature",
    subtitle = paste(
      "Forecast produced on",
      format(daily_temp_end$elaborado[1], usetz = TRUE)
    )
  )

# Spatial with mapSpain
library(mapSpain)
library(sf)

lugo_sf <- esp_get_munic(munic = "Lugo") %>%
  select(LAU_CODE)

daily_temp_end_lugo_sf <- daily_temp_end %>%
  filter(nombre == "Lugo" & name == "temperatura_maxima") %>%
  # Join by LAU_CODE
  left_join(lugo_sf, by = c("municipio" = "LAU_CODE")) %>%
  st_as_sf()

ggplot(daily_temp_end_lugo_sf) +
  geom_sf(aes(fill = value)) +
  facet_wrap(~fecha) +
  scale_fill_gradientn(
    colors = c("blue", "red"),
    guide = guide_legend()
  ) +
  labs(
    main = "Forecast: 7-day max temperature",
    subtitle = "Lugo, ES"
  )

# Select a city
data("aemet_munic")
library(dplyr)
munis <- aemet_munic %>%
  filter(municipio_nombre %in% c("Santiago de Compostela", "Lugo")) %>%
  pull(municipio)

daily <- aemet_forecast_daily(munis)

# Metadata
meta <- aemet_forecast_daily(munis, extract_metadata = TRUE)
glimpse(meta$campos)

# Vars available
aemet_forecast_vars_available(daily)


# This is nested
daily %>%
  select(municipio, fecha, nombre, temperatura)

# Select and unnest
daily_temp <- aemet_forecast_tidy(daily, "temperatura")

# This is not
daily_temp

# Wrangle and plot
daily_temp_end <- daily_temp %>%
  select(
    elaborado, fecha, municipio, nombre, temperatura_minima,
    temperatura_maxima
  ) %>%
  tidyr::pivot_longer(cols = contains("temperatura"))

# Plot
library(ggplot2)
ggplot(daily_temp_end) +
  geom_line(aes(fecha, value, color = name)) +
  facet_wrap(~nombre, ncol = 1) +
  scale_color_manual(
    values = c("red", "blue"),
    labels = c("max", "min")
  ) +
  scale_x_date(
    labels = scales::label_date_short(),
    breaks = "day"
  ) +
  scale_y_continuous(
    labels = scales::label_comma(suffix = "º")
  ) +
  theme_minimal() +
  labs(
    x = "", y = "",
    color = "",
    title = "Forecast: 7-day temperature",
    subtitle = paste(
      "Forecast produced on",
      format(daily_temp_end$elaborado[1], usetz = TRUE)
    )
  )

# Spatial with mapSpain
library(mapSpain)
library(sf)

lugo_sf <- esp_get_munic(munic = "Lugo") %>%
  select(LAU_CODE)

daily_temp_end_lugo_sf <- daily_temp_end %>%
  filter(nombre == "Lugo" & name == "temperatura_maxima") %>%
  # Join by LAU_CODE
  left_join(lugo_sf, by = c("municipio" = "LAU_CODE")) %>%
  st_as_sf()

ggplot(daily_temp_end_lugo_sf) +
  geom_sf(aes(fill = value)) +
  facet_wrap(~fecha) +
  scale_fill_gradientn(
    colors = c("blue", "red"),
    guide = guide_legend()
  ) +
  labs(
    main = "Forecast: 7-day max temperature",
    subtitle = "Lugo, ES"
  )

Helper functions for extracting forecasts

Description

Helpers for aemet_forecast_daily() and aemet_forecast_hourly():

aemet_forecast_vars_available() extracts the values available on the dataset.
aemet_forecast_tidy() produces a tibble with the forecast for var.

Usage

aemet_forecast_tidy(x, var)

aemet_forecast_vars_available(x)
aemet_forecast_tidy(x, var)

aemet_forecast_vars_available(x)

Arguments

`x`	A database extracted with `aemet_forecast_daily()` or `aemet_forecast_hourly()`.
`var`	Name of the desired var to extract

Value

A vector of characters (aemet_forecast_vars_available()) or a tibble (aemet_forecast_tidy()).

Examples


# Hourly values
hourly <- aemet_forecast_hourly(c("15030", "28080"))

# Vars available
aemet_forecast_vars_available(hourly)

# Get temperature
temp <- aemet_forecast_tidy(hourly, "temperatura")

library(dplyr)
# Make hour - Need lubridate to adjust timezones
temp_end <- temp %>%
  mutate(
    forecast_time = lubridate::force_tz(
      as.POSIXct(fecha) + hora,
      tz = "Europe/Madrid"
    )
  )

# Add also sunset and sunrise
suns <- temp_end %>%
  select(nombre, fecha, orto, ocaso) %>%
  distinct_all() %>%
  group_by(nombre) %>%
  mutate(
    ocaso_end = lubridate::force_tz(
      as.POSIXct(fecha) + ocaso,
      tz = "Europe/Madrid"
    ),
    orto_end = lubridate::force_tz(
      as.POSIXct(fecha) + orto,
      tz = "Europe/Madrid"
    ),
    orto_lead = lead(orto_end)
  ) %>%
  tidyr::drop_na()



# Plot

library(ggplot2)

ggplot(temp_end) +
  geom_rect(data = suns, aes(
    xmin = ocaso_end, xmax = orto_lead,
    ymin = min(temp_end$temperatura),
    ymax = max(temp_end$temperatura)
  ), alpha = .4) +
  geom_line(aes(forecast_time, temperatura), color = "blue4") +
  facet_wrap(~nombre, nrow = 2) +
  scale_x_datetime(labels = scales::label_date_short()) +
  scale_y_continuous(labels = scales::label_number(suffix = "º")) +
  labs(
    x = "", y = "",
    title = "Forecast: Temperature",
    subtitle = paste("Forecast produced on", format(temp_end$elaborado[1],
      usetz = TRUE
    ))
  )

# Hourly values
hourly <- aemet_forecast_hourly(c("15030", "28080"))

# Vars available
aemet_forecast_vars_available(hourly)

# Get temperature
temp <- aemet_forecast_tidy(hourly, "temperatura")

library(dplyr)
# Make hour - Need lubridate to adjust timezones
temp_end <- temp %>%
  mutate(
    forecast_time = lubridate::force_tz(
      as.POSIXct(fecha) + hora,
      tz = "Europe/Madrid"
    )
  )

# Add also sunset and sunrise
suns <- temp_end %>%
  select(nombre, fecha, orto, ocaso) %>%
  distinct_all() %>%
  group_by(nombre) %>%
  mutate(
    ocaso_end = lubridate::force_tz(
      as.POSIXct(fecha) + ocaso,
      tz = "Europe/Madrid"
    ),
    orto_end = lubridate::force_tz(
      as.POSIXct(fecha) + orto,
      tz = "Europe/Madrid"
    ),
    orto_lead = lead(orto_end)
  ) %>%
  tidyr::drop_na()



# Plot

library(ggplot2)

ggplot(temp_end) +
  geom_rect(data = suns, aes(
    xmin = ocaso_end, xmax = orto_lead,
    ymin = min(temp_end$temperatura),
    ymax = max(temp_end$temperatura)
  ), alpha = .4) +
  geom_line(aes(forecast_time, temperatura), color = "blue4") +
  facet_wrap(~nombre, nrow = 2) +
  scale_x_datetime(labels = scales::label_date_short()) +
  scale_y_continuous(labels = scales::label_number(suffix = "º")) +
  labs(
    x = "", y = "",
    title = "Forecast: Temperature",
    subtitle = paste("Forecast produced on", format(temp_end$elaborado[1],
      usetz = TRUE
    ))
  )

Last observation values for a station

Description

Get last observation values for a station.

Usage

aemet_last_obs(
  station = "all",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_last_obs(
  station = "all",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`) or "all" for all the stations.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.

Value

A tibble or a sf object

API Key

You need to set your API Key globally using aemet_api_key().

Examples



library(tibble)
obs <- aemet_last_obs(c("9434", "3195"))
glimpse(obs)

library(tibble)
obs <- aemet_last_obs(c("9434", "3195"))
glimpse(obs)

Monthly/annual climatology

Description

Get monthly/annual climatology values for a station or all the stations. aemet_monthly_period() and aemet_monthly_period_all() allows requests that span several years.

Usage

aemet_monthly_clim(
  station = NULL,
  year = as.integer(format(Sys.Date(), "%Y")),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period(
  station = NULL,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_monthly_clim(
  station = NULL,
  year = as.integer(format(Sys.Date(), "%Y")),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period(
  station = NULL,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`).
`year`	Numeric value as date (format: `YYYY`).
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.
`start`	Numeric value as start year (format: `YYYY`).
`end`	Numeric value as end year (format: `YYYY`).

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples



library(tibble)
obs <- aemet_monthly_clim(station = c("9434", "3195"), year = 2000)
glimpse(obs)

library(tibble)
obs <- aemet_monthly_clim(station = c("9434", "3195"), year = 2000)
glimpse(obs)

Data set with all the municipalities of Spain

Description

A tibble with all the municipalities of Spain as defined by the INE (Instituto Nacional de Estadistica) as of January 2024.

Format

A tibble with 8,132 rows and fields:

municipio: INE code of the municipality.
municipio_nombre: INE name of the municipality.
cpro: INE code of the province.
cpro_nombre: INE name of the province.
codauto: INE code of the autonomous community.
codauto_nombre: INE code of the autonomous community.

Source

INE, Municipality codes by province

Examples


data(aemet_munic)

aemet_munic
data(aemet_munic)

aemet_munic

Normal climatology values

Description

Get normal climatology values for a station (or all the stations with aemet_normal_clim_all(). Standard climatology from 1981 to 2010.

Usage

aemet_normal_clim(
  station = NULL,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_normal_clim_all(
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)
aemet_normal_clim(
  station = NULL,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_normal_clim_all(
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`) or "all" for all the stations.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.
`extract_metadata`	Logical `TRUE/FALSE`. On `TRUE` the output is a `tibble` with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical, display a `cli::cli_progress_bar()` object. If `verbose = TRUE` won't be displayed.

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Note

Code modified from project https://github.com/SevillaR/aemet.

Examples



library(tibble)
obs <- aemet_normal_clim(c("9434", "3195"))
glimpse(obs)

library(tibble)
obs <- aemet_normal_clim(c("9434", "3195"))
glimpse(obs)

AEMET stations

Description

Get AEMET stations.

Usage

aemet_stations(verbose = FALSE, return_sf = FALSE)
aemet_stations(verbose = FALSE, return_sf = FALSE)

Arguments

`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`return_sf`	Logical `TRUE` or `FALSE`. Should the function return an `sf` spatial object? If `FALSE` (the default value) it returns a `tibble`. Note that you need to have the sf package installed.

Details

The first result of the API call on each session is (temporarily) cached in the assigned tempdir() for avoiding unneeded API calls.

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Note

Code modified from project https://github.com/SevillaR/aemet.

Examples


library(tibble)
stations <- aemet_stations()
stations

# Cached during this R session
stations2 <- aemet_stations(verbose = TRUE)

identical(stations, stations2)

library(tibble)
stations <- aemet_stations()
stations

# Cached during this R session
stations2 <- aemet_stations(verbose = TRUE)

identical(stations, stations2)

Climatogram data for Zaragoza Airport ("9434") period 1981-2010

Description

Normal data for Zaragoza Airport (1981-2010). This is an example dataset used to plot climatograms.

Format

A data.frame with columns 1 to 12 (months) and rows:

p_mes_md: Precipitation (mm).
tm_max_md: Maximum temperature (Celsius).
tm_min_md: Minimum temperature (Celsius).
ta_min_md: Absolute monthly minimum temperature (Celsius).

Source

AEMET.

Examples

data(climaemet_9434_climatogram)
data(climaemet_9434_climatogram)

Average annual temperatures for Zaragoza Airport ("9434") period 1950-2020

Description

Yearly observations of average temperature for Zaragoza Airport (1950-2020). This is an example dataset.

Format

A tibble with columns:

year: Year of reference.
indicativo: Identifier of the station.
temp: Average temperature (Celsius).

Source

AEMET.

Examples

data(climaemet_9434_temp)
data(climaemet_9434_temp)

Wind conditions for Zaragoza Airport ("9434") period 2000-2020

Description

Daily observations of wind speed and directions for Zaragoza Airport (2000-2020). This is an example dataset.

Format

A tibble with columns:

fecha: Date of observation.
dir: Wind directions (0-360).
velmedia: Average wind speed (km/h)

Source

AEMET.

Examples

data(climaemet_9434_wind)
data(climaemet_9434_wind)

Station climate stripes graph

Description

Plot climate stripes graph for a station.

Usage

climatestripes_station(
  station,
  start = 1950,
  end = 2020,
  with_labels = "yes",
  verbose = FALSE,
  ...
)
climatestripes_station(
  station,
  start = 1950,
  end = 2020,
  with_labels = "yes",
  verbose = FALSE,
  ...
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`).
`start`	Numeric value as start year (format: `YYYY`).
`end`	Numeric value as end year (format: `YYYY`).
`with_labels`	Character string as yes/no. Indicates whether to use labels for the graph or not.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`...`	Arguments passed on to `ggstripes` `n_temp` Numeric value as the number of colors of the palette. (default `11`). `col_pal` Character string indicating the name of the `hcl.pals()` color palette to be used for plotting.

Value

A ggplot2 object

API Key

You need to set your API Key globally using aemet_api_key().

Examples



climatestripes_station(
  "9434",
  start = 2010,
  end = 2020,
  with_labels = "yes",
  col_pal = "Inferno"
)


climatestripes_station(
  "9434",
  start = 2010,
  end = 2020,
  with_labels = "yes",
  col_pal = "Inferno"
)

Walter & Lieth climatic diagram from normal climatology values

Description

Plot of a Walter & Lieth climatic diagram from normal climatology data for a station. This climatogram are great for showing a summary of climate conditions for a place over a time period (1981-2010).

Usage

climatogram_normal(
  station,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)
climatogram_normal(
  station,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`).
`labels`	Character string as month labels for the X axis: `"en"` (english), `"es"` (spanish), `"fr"` (french), etc.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`ggplot2`	`TRUE/FALSE`. On `TRUE` the function uses `ggclimat_walter_lieth()`, if `FALSE` uses `climatol::diagwl()`.
`...`	Further arguments to `climatol::diagwl()` or `ggclimat_walter_lieth()`, depending on the value of ggplot2.

Value

A plot.

API Key

You need to set your API Key globally using aemet_api_key().

Note

The code is based on code from the CRAN package climatol.

References

Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.
Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

Examples


climatogram_normal("9434")

climatogram_normal("9434")

Walter & Lieth climatic diagram for a time period

Description

Plot of a Walter & Lieth climatic diagram from monthly climatology data for a station. This climatogram are great for showing a summary of climate conditions for a place over a specific time period.

Usage

climatogram_period(
  station = NULL,
  start = 1990,
  end = 2020,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)
climatogram_period(
  station = NULL,
  start = 1990,
  end = 2020,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`).
`start`	Numeric value as start year (format: `YYYY`).
`end`	Numeric value as end year (format: `YYYY`).
`labels`	Character string as month labels for the X axis: `"en"` (english), `"es"` (spanish), `"fr"` (french), etc.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.
`ggplot2`	`TRUE/FALSE`. On `TRUE` the function uses `ggclimat_walter_lieth()`, if `FALSE` uses `climatol::diagwl()`.
`...`	Further arguments to `climatol::diagwl()` or `ggclimat_walter_lieth()`, depending on the value of ggplot2.

Value

A plot.

API Key

You need to set your API Key globally using aemet_api_key().

Note

The code is based on code from the CRAN package climatol.

References

Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.
Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

Examples



climatogram_period("9434", start = 2015, end = 2020, labels = "en")


climatogram_period("9434", start = 2015, end = 2020, labels = "en")

Converts `dms` format to decimal degrees

Description

Converts degrees, minutes and seconds to decimal degrees.

Usage

dms2decdegrees(input = NULL)

dms2decdegrees_2(input = NULL)
dms2decdegrees(input = NULL)

dms2decdegrees_2(input = NULL)

Arguments

input

Character string as dms coordinates.

Value

A numeric value.

Note

Code for dms2decdegrees() modified from project https://github.com/SevillaR/aemet.

Examples

dms2decdegrees("055245W")
dms2decdegrees_2("-3º 40' 37\"")
dms2decdegrees("055245W")
dms2decdegrees_2("-3º 40' 37\"")

First and last day of year

Description

Get first and last day of year.

Usage

first_day_of_year(year = NULL)

last_day_of_year(year = NULL)
first_day_of_year(year = NULL)

last_day_of_year(year = NULL)

Arguments

year

Numeric value as year (format: YYYY).

Value

Character string as date (format: YYYY-MM-DD).

Examples

first_day_of_year(2000)
last_day_of_year(2020)
first_day_of_year(2000)
last_day_of_year(2020)

Client tool for AEMET API

Description

Client tool to get data and metadata from AEMET and convert json to tibble.

Usage

get_data_aemet(apidest, verbose = FALSE)

get_metadata_aemet(apidest, verbose = FALSE)
get_data_aemet(apidest, verbose = FALSE)

get_metadata_aemet(apidest, verbose = FALSE)

Arguments

`apidest`	Character string as destination URL. See https://opendata.aemet.es/dist/index.html.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.

Value

A tibble (if possible) or the results of the query as provided by httr2::resp_body_raw() or httr2::resp_body_string().

Source

https://opendata.aemet.es/dist/index.html.

Examples


# Run this example only if AEMET_API_KEY is detected

url <- "/api/valores/climatologicos/inventarioestaciones/todasestaciones"

get_data_aemet(url)


# Metadata

get_metadata_aemet(url)

# We can get data from any API endpoint

# Plain text

plain <- get_data_aemet("/api/prediccion/nacional/hoy")

cat(plain)

# An image

image <- get_data_aemet("/api/mapasygraficos/analisis")

# Write and read
tmp <- tempfile(fileext = ".gif")

writeBin(image, tmp)

gganimate::gif_file(tmp)

# Run this example only if AEMET_API_KEY is detected

url <- "/api/valores/climatologicos/inventarioestaciones/todasestaciones"

get_data_aemet(url)


# Metadata

get_metadata_aemet(url)

# We can get data from any API endpoint

# Plain text

plain <- get_data_aemet("/api/prediccion/nacional/hoy")

cat(plain)

# An image

image <- get_data_aemet("/api/mapasygraficos/analisis")

# Write and read
tmp <- tempfile(fileext = ".gif")

writeBin(image, tmp)

gganimate::gif_file(tmp)

Walter and Lieth climatic diagram on ggplot2

Description

Plot of a Walter and Lieth climatic diagram of a station. This function is an updated version of climatol::diagwl(), by Jose A. Guijarro.

[Experimental]

Usage

ggclimat_walter_lieth(
  dat,
  est = "",
  alt = NA,
  per = NA,
  mlab = "es",
  pcol = "#002F70",
  tcol = "#ff0000",
  pfcol = "#9BAEE2",
  sfcol = "#3C6FC4",
  shem = FALSE,
  p3line = FALSE,
  ...
)
ggclimat_walter_lieth(
  dat,
  est = "",
  alt = NA,
  per = NA,
  mlab = "es",
  pcol = "#002F70",
  tcol = "#ff0000",
  pfcol = "#9BAEE2",
  sfcol = "#3C6FC4",
  shem = FALSE,
  p3line = FALSE,
  ...
)

Arguments

`dat`	Monthly climatic data for which the diagram will be plotted.
`est`	Name of the climatological station.
`alt`	Altitude of the climatological station.
`per`	Period on which the averages have been computed.
`mlab`	Month labels for the X axis. Use 2-digit language code (`"en"`, `"es"`, etc.). See `readr::locale()` for info.
`pcol`	Color for precipitation.
`tcol`	Color for temperature.
`pfcol`	Fill color for probable frosts.
`sfcol`	Fill color for sure frosts.
`shem`	Set to `TRUE` for southern hemisphere stations.
`p3line`	Set to `TRUE` to draw a supplementary precipitation line referenced to three times the temperature (as suggested by Bogdan Rosca).
`...`	Other graphic parameters

Details

See Details on climatol::diagwl().

Climatic data must be passed as a 4x12 matrix or data.frame of monthly (January to December) data, in the following order:

Row 1: Mean precipitation.
Row 2: Mean maximum daily temperature.
Row 3: Mean minimum daily temperature.
Row 4: Absolute monthly minimum temperature.

See climaemet_9434_climatogram for a sample dataset.

Value

A ggplot2 object. See help("ggplot2").

API Key

You need to set your API Key globally using aemet_api_key().

References

Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

Examples


library(ggplot2)

wl <- ggclimat_walter_lieth(
  climaemet::climaemet_9434_climatogram,
  alt = "249",
  per = "1981-2010",
  est = "Zaragoza Airport"
)

wl

# As it is a ggplot object we can modify it

wl + theme(
  plot.background = element_rect(fill = "grey80"),
  panel.background = element_rect(fill = "grey70"),
  axis.text.y.left = element_text(
    colour = "black",
    face = "italic"
  ),
  axis.text.y.right = element_text(
    colour = "black",
    face = "bold"
  )
)
library(ggplot2)

wl <- ggclimat_walter_lieth(
  climaemet::climaemet_9434_climatogram,
  alt = "249",
  per = "1981-2010",
  est = "Zaragoza Airport"
)

wl

# As it is a ggplot object we can modify it

wl + theme(
  plot.background = element_rect(fill = "grey80"),
  panel.background = element_rect(fill = "grey70"),
  axis.text.y.left = element_text(
    colour = "black",
    face = "italic"
  ),
  axis.text.y.right = element_text(
    colour = "black",
    face = "bold"
  )
)

Warming stripes graph

Description

Plot different "climate stripes" or "warming stripes" using ggplot2. This graphics are visual representations of the change in temperature as measured in each location over the past 70-100+ years. Each stripe represents the temperature in that station averaged over a year.

Usage

ggstripes(
  data,
  plot_type = "stripes",
  plot_title = "",
  n_temp = 11,
  col_pal = "RdBu",
  ...
)
ggstripes(
  data,
  plot_type = "stripes",
  plot_title = "",
  n_temp = 11,
  col_pal = "RdBu",
  ...
)

Arguments

`data`	a data.frame with date(`year`) and temperature(`temp`) variables.
`plot_type`	plot type (with labels, background, stripes with line trend and animation). Accepted values are `"background"`, `"stripes"`, `"trend"` or `"animation"`.
`plot_title`	character string to be used for the graph title.
`n_temp`	Numeric value as the number of colors of the palette. (default `11`).
`col_pal`	Character string indicating the name of the `hcl.pals()` color palette to be used for plotting.
`...`	further arguments passed to `ggplot2::theme()`.

Value

A ggplot2 object

API Key

You need to set your API Key globally using aemet_api_key().

Note

"Warming stripes" charts are a conceptual idea of Professor Ed Hawkins (University of Reading) and are specifically designed to be as simple as possible and alert about risks of climate change. For more details see ShowYourStripes.

Examples


library(ggplot2)

data <- climaemet::climaemet_9434_temp

ggstripes(data, plot_title = "Zaragoza Airport") +
  labs(subtitle = "(1950-2020)")

ggstripes(data, plot_title = "Zaragoza Airport", plot_type = "trend") +
  labs(subtitle = "(1950-2020)")

library(ggplot2)

data <- climaemet::climaemet_9434_temp

ggstripes(data, plot_title = "Zaragoza Airport") +
  labs(subtitle = "(1950-2020)")

ggstripes(data, plot_title = "Zaragoza Airport", plot_type = "trend") +
  labs(subtitle = "(1950-2020)")

Windrose (speed/direction) diagram

Description

Plot a windrose showing the wind speed and direction using ggplot2.

Usage

ggwindrose(
  speed,
  direction,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  facet = NULL,
  plot_title = "",
  ...
)
ggwindrose(
  speed,
  direction,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  facet = NULL,
  plot_title = "",
  ...
)

Arguments

`speed`	Numeric vector of wind speeds.
`direction`	Numeric vector of wind directions.
`n_directions`	Numeric value as the number of direction bins to plot (petals on the rose). The number of directions defaults to `8`.
`n_speeds`	Numeric value as the number of equally spaced wind speed bins to plot. This is used if `speed_cuts` is `NA` (default `5`).
`speed_cuts`	Numeric vector containing the cut points for the wind speed intervals, or `NA` (default).
`col_pal`	Character string indicating the name of the `hcl.pals()` color palette to be used for plotting.
`legend_title`	Character string to be used for the legend title.
`calm_wind`	Numeric value as the upper limit for wind speed that is considered calm (default `0`).
`n_col`	The number of columns of plots (default 1).
`facet`	Character or factor vector of the facets used to plot the various windroses.
`plot_title`	Character string to be used for the plot title.
`...`	further arguments (ignored).

Value

A ggplot2 object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples



library(ggplot2)

speed <- climaemet::climaemet_9434_wind$velmedia
direction <- climaemet::climaemet_9434_wind$dir

rose <- ggwindrose(
  speed = speed,
  direction = direction,
  speed_cuts = seq(0, 16, 4),
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  plot_title = "Zaragoza Airport"
)
rose + labs(
  subtitle = "2000-2020",
  caption = "Source: AEMET"
)
library(ggplot2)

speed <- climaemet::climaemet_9434_wind$velmedia
direction <- climaemet::climaemet_9434_wind$dir

rose <- ggwindrose(
  speed = speed,
  direction = direction,
  speed_cuts = seq(0, 16, 4),
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  plot_title = "Zaragoza Airport"
)
rose + labs(
  subtitle = "2000-2020",
  caption = "Source: AEMET"
)

Windrose (speed/direction) diagram of a station over a days period

Description

Plot a windrose showing the wind speed and direction for a station over a days period.

Usage

windrose_days(
  station,
  start = "2000-12-01",
  end = "2000-12-31",
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind Speed (m/s)",
  verbose = FALSE
)
windrose_days(
  station,
  start = "2000-12-01",
  end = "2000-12-31",
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind Speed (m/s)",
  verbose = FALSE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`) or "all" for all the stations.
`start`	Character string as start date (format: `"YYYY-MM-DD"`).
`end`	Character string as end date (format: `"YYYY-MM-DD"`).
`n_directions`	Numeric value as the number of direction bins to plot (petals on the rose). The number of directions defaults to `8`.
`n_speeds`	Numeric value as the number of equally spaced wind speed bins to plot. This is used if `speed_cuts` is `NA` (default `5`).
`speed_cuts`	Numeric vector containing the cut points for the wind speed intervals, or `NA` (default).
`col_pal`	Character string indicating the name of the `hcl.pals()` color palette to be used for plotting.
`calm_wind`	Numeric value as the upper limit for wind speed that is considered calm (default `0`).
`legend_title`	Character string to be used for the legend title.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.

Value

A ggplot2 object.

API Key

You need to set your API Key globally using aemet_api_key().

Examples


windrose_days("9434",
  start = "2000-12-01",
  end = "2000-12-31",
  speed_cuts = 4
)

windrose_days("9434",
  start = "2000-12-01",
  end = "2000-12-31",
  speed_cuts = 4
)

Windrose (speed/direction) diagram of a station over a time period

Description

Plot a windrose showing the wind speed and direction for a station over a time period.

Usage

windrose_period(
  station,
  start = 2000,
  end = 2010,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind Speed (m/s)",
  verbose = FALSE
)
windrose_period(
  station,
  start = 2000,
  end = 2010,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind Speed (m/s)",
  verbose = FALSE
)

Arguments

`station`	Character string with station identifier code(s) (see `aemet_stations()`) or "all" for all the stations.
`start`	Numeric value as start year (format: `YYYY`).
`end`	Numeric value as end year (format: `YYYY`).
`n_directions`	Numeric value as the number of direction bins to plot (petals on the rose). The number of directions defaults to `8`.
`n_speeds`	Numeric value as the number of equally spaced wind speed bins to plot. This is used if `speed_cuts` is `NA` (default `5`).
`speed_cuts`	Numeric vector containing the cut points for the wind speed intervals, or `NA` (default).
`col_pal`	Character string indicating the name of the `hcl.pals()` color palette to be used for plotting.
`calm_wind`	Numeric value as the upper limit for wind speed that is considered calm (default `0`).
`legend_title`	Character string to be used for the legend title.
`verbose`	Logical `TRUE/FALSE`. Provides information about the flow of information between the client and server.

Value

A ggplot2 object

API Key

You need to set your API Key globally using aemet_api_key().

Examples



windrose_period("9434",
  start = 2000, end = 2010,
  speed_cuts = 4
)


windrose_period("9434",
  start = 2000, end = 2010,
  speed_cuts = 4
)

Package 'climaemet'

Help Index

Install an AEMET API Key

Description

Usage

Arguments

Details

Value

Note

See Also

Examples

AEMET beaches

Description

Usage

Arguments

Details

Value

API Key

See Also

Examples

Daily/annual climatology values

Description

Usage

Arguments

Details

Value

API Key

See Also

Examples

Check if an AEMET API Key is present for the current session

Description

Usage

Arguments

Value

See Also

Examples

Extreme values for a station

Description

Usage

Arguments

Value

API Key

See Also

Examples

Forecast database for beaches

Description

Usage

Arguments

Value

API Key

See Also

Examples

Forecast database by municipality

Description

Usage

Arguments

Details

Value

API Key

See Also

Examples

Helper functions for extracting forecasts

Description

Usage

Arguments

Value

See Also

Examples

Last observation values for a station

Description

Usage

Arguments

Value

API Key

See Also

Examples

Monthly/annual climatology

Description

Usage

Arguments